The media content distribution industry has seen a massive growth in the last few years. Unlike the traditional television and set top boxes, where content is available as per channel schedules, users can now access content, such as television program content, over the Internet using a multitude of devices at any time during the day.
As media content accounts generally supplement cable television connections, the accounts are typically shared across a household, and members of the household watch media content online through the same account. These members usually have varied channel preferences. For example, the teenagers in the house might prefer sports or news channels, whereas the younger kids might prefer cartoon channels. In such a situation, where multiple viewers with different viewing patterns are using the same account, tracking individual user behavior becomes a challenging problem as only account-level statistics are captured by standard data analytics methods.
This poses a challenge to personalization for media content accounts because making targeted channel recommendations can only be done only at the account level, and not at the individual level. For any effective personalization and engagement technology, differentiation for viewing characteristics of each individual in the family or relevant viewing group is important.